VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection

• URL: http://arxiv.org/abs/2508.11167v2
• Date: Tue, 26 Aug 2025 08:55:26 GMT
• Title: VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection
• Authors: Jianhong Han, Yupei Wang, Liang Chen,
• Abstract summary: VG-DETR integrates a Vision Foundation Model (VFM) into the training pipeline in a "free lunch" manner.<n>We introduce a VFM-guided pseudo-label mining strategy that leverages the VFM's semantic priors to assess the reliability of the generated pseudo-labels.<n>In addition, a dual-level VFM-guided alignment method is proposed, which aligns detector features with VFM embeddings at both the instance and image levels.
• Score: 9.029534000674388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised domain adaptation methods have been widely explored to bridge domain gaps. However, in real-world remote-sensing scenarios, privacy and transmission constraints often preclude access to source domain data, which limits their practical applicability. Recently, Source-Free Object Detection (SFOD) has emerged as a promising alternative, aiming at cross-domain adaptation without relying on source data, primarily through a self-training paradigm. Despite its potential, SFOD frequently suffers from training collapse caused by noisy pseudo-labels, especially in remote sensing imagery with dense objects and complex backgrounds. Considering that limited target domain annotations are often feasible in practice, we propose a Vision foundation-Guided DEtection TRansformer (VG-DETR), built upon a semi-supervised framework for SFOD in remote sensing images. VG-DETR integrates a Vision Foundation Model (VFM) into the training pipeline in a "free lunch" manner, leveraging a small amount of labeled target data to mitigate pseudo-label noise while improving the detector's feature-extraction capability. Specifically, we introduce a VFM-guided pseudo-label mining strategy that leverages the VFM's semantic priors to further assess the reliability of the generated pseudo-labels. By recovering potentially correct predictions from low-confidence outputs, our strategy improves pseudo-label quality and quantity. In addition, a dual-level VFM-guided alignment method is proposed, which aligns detector features with VFM embeddings at both the instance and image levels. Through contrastive learning among fine-grained prototypes and similarity matching between feature maps, this dual-level alignment further enhances the robustness of feature representations against domain gaps. Extensive experiments demonstrate that VG-DETR achieves superior performance in source-free remote sensing detection tasks.

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